| Title: | Association Between Plasma Metabolomic Profile and Machine Learning-Based Brain Age |
| Journal: | Aging Cell |
| Published: | 1 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40888555/ |
| DOI: | https://doi.org/10.1111/acel.70208 |
| URL: | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acel.70208 |
Publication 19608
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Abstract
Metabolomics has been associated with cognitive decline and dementia, but the relationship between metabolites and brain aging remains unclear. We aimed to investigate the associations of metabolomics with brain age assessed by neuroimaging and to explore whether these relationships vary according to apolipoprotein E (APOE) ε4. This study included 17,770 chronic brain disorder-free participants aged 40-69 years from UK Biobank who underwent neuroimaging scans an average of 9 years after baseline. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Brain age was estimated using LASSO regression and 1079 brain MRI phenotypes and brain age gap (BAG; i.e., brain age minus chronological age) was calculated. Data were analyzed using linear regression. We identified 64 and 77 metabolites associated with brain age and BAG, respectively, of which 55 overlapped. Lipids (including cholesterol, cholesteryl esters, free cholesterol, phospholipids, and total lipids) in S/M-HDL, as well as phospholipids and triglycerides as a percentage of total lipids in different-density lipoproteins, were associated with larger BAG. The percentages of cholesterol, cholesteryl esters, and free cholesterol to total lipids in VLDL, LDL, and HDL of different particle sizes were associated with smaller BAG. The associations of LA/FA, omega-6/FA, SFA/FA, and phospholipids to total lipids in L-HDL with brain age were consistent across APOE ε4 carriers and non-carriers (all p for interaction > 0.05). Plasma metabolites show remarkably widespread associations with brain aging regardless of APOE ε4 genetic risk. Metabolic profiles could serve as an early indicator of accelerated brain aging.</p>
8 Authors
- Yang Li
- Jiao Wang
- Yuyang Miao
- Michelle M. Dunk
- Yan Liu
- Zhongze Fang
- Qiang Zhang
- Weili Xu
1 Application
| Application ID | Title |
|---|---|
| 67048 | THE BODY-MIND CONNECTION: EXPLORING THE BIOLOGICAL MECHANISMS UNDERLYING THE EFFECTS OF SOMATIC HEALTH ON BRAIN AGING |
Enabling scientific discoveries that improve human health